Abstract: A computer obtains multipliers of a sensitive feature. From an input that contains a value of the feature, a probability of a class is inferred. Based on the value of the feature in the input, one of the multipliers of the feature is selected. The multiplier is specific to both of the feature and the value of the feature. The input is classified based on a multiplicative product of the probability of the class and the multiplier that is specific to both of the feature and the value of the feature. In an embodiment, a black-box tri-objective optimizer generates multipliers on a three-way Pareto frontier from which a user may interactively select a combination of multipliers that provides a best three-way tradeoff between fairness and accuracy. The optimizer has three objectives to respectively optimize three distinct validation metrics that may, for example, be accuracy, fairness, and favorable outcome rate decrease.

Abstract: In an embodiment, a computer generates a respective original inference from each of many records. Permuted values are selected for a feature from original values of the feature. Based on the permuted values for the feature, a permuted inference is generated from each record. Fairness and accuracy of the original and permuted inferences are measured. For each of many features, the computer measures a respective impact on fairness of a machine learning model, and a respective impact on accuracy of the machine learning model. A global explanation of the machine learning model is generated and presented based on, for multiple features, the impacts on fairness and accuracy. Based on the global explanation, an interactive indication to exclude or include a particular feature is received. The machine learning model is (re-)trained based on the interactive indication to exclude or include the particular feature, which may increase the fairness of the model.

Abstract: Techniques for selecting machine-learned (ML) models using diversity criteria are provided. In one technique, for each ML model of multiple ML models, output data is generated based on input data to the ML model. Multiple pairs of ML models are identified, where each ML model in the multiple pairs is from the multiple ML models. For each pair of ML models in the multiple pairs of ML models: (1) first output data that was previously generated by a first ML model in the pair is identified; (2) second output data that was previously generated by a second ML model in the pair is identified; (3) a diversity value that is based on the first and second output data is generated; and (4) the diversity value is added to a set of diversity values. A subset of the multiple ML models is selected based on the set of diversity values.

Abstract: From many features and many multidimensional points, a computer generates exploratory training configurations. Each point contains a value for each of the features. Each exploratory training configuration identifies a random subset of the features and a random subset of the points. A performance score is generated for each of the exploratory training configurations. A feature weight is generated for each of the features that is based on the performance scores of the exploratory training configurations whose random subset of features contains the feature. A point weight is generated for each of the points that is based on the performance scores of the exploratory training configurations whose random subset of the many points contains the point. A machine learning model is trained using an optimized training corpus that consists of a subset of the many features based on feature weight and a subset of the many points based on point weight.

Abstract: Distributions of on-chip inductors for monolithic voltage regulation are described. On-chip voltage regulation may be provided by integrated voltage regulators (IVRs), such as a buck converter with integrated inductors. On-chip inductors may be placed to ensure optimal voltage regulation for high power density applications. With this technology, integrated circuits may have many independent voltage domains for fine-grained dynamic voltage and frequency scaling that allows for higher overall power efficiency for the system.

Abstract: In an embodiment, a computer infers, from an input (e.g. that represents a person) that contains a value of a sensitive feature that has a plurality of multipliers, a probability of a majority class (i.e. an outcome). Based on the value of the sensitive feature in the input, from the multipliers of the sensitive feature, a multiplier is selected that is specific to both of the sensitive feature and the value of the sensitive feature. The input is classified based on a multiplicative product of the probability of the majority class and the multiplier that is specific to both of the sensitive feature and the value of the sensitive feature. In an embodiment, a black-box bi-objective optimizer generates multipliers on a Pareto frontier from which a user may interactively select a combination of multipliers that provide a best tradeoff between fairness and accuracy.

Abstract: In an embodiment, a computer combines first original hyperparameters and second original hyperparameters into combined hyperparameters. In each iteration of a binary search that selects hyperparameters, these are selected: a) important hyperparameters from the combined hyperparameters and b) based on an estimated complexity decrease by including only important hyperparameters as compared to the combined hyperparameters, which only one boundary of the binary search to adjust. For the important hyperparameters of a last iteration of the binary search that selects hyperparameters, a pruned value range of a particular hyperparameter is generated based on a first original value range of the particular hyperparameter for the first original hyperparameters and a second original value range of the same particular hyperparameter for the second original hyperparameters.

Abstract: Distributions of on-chip inductors for monolithic voltage regulation are described. On-chip voltage regulation may be provided by integrated voltage regulators (IVRs), such as a buck converter with integrated inductors. On-chip inductors may be placed to ensure optimal voltage regulation for high power density applications. With this technology, integrated circuits may have many independent voltage domains for fine-grained dynamic voltage and frequency scaling that allows for higher overall power efficiency for the system.

Abstract: In an embodiment, a computer generates, from an input, an inference that contains multiple probabilities respectively for multiple mutually exclusive classes that contain a first class and a second class. The probabilities contain (e.g. due to overfitting) a higher probability for the first class that is higher than a lower probability for the second class. In response to a threshold exceeding the higher probability, the input is automatically and more accurately classified as the second class. One, some, or almost all classes may have a respective distinct threshold that can be concurrently applied for acceleration. Data parallelism may simultaneously apply a threshold to a batch of multiple inputs for acceleration.

Abstract: A computer stores a reference corpus that consists of many reference points that each has a respective class. Later, an expected class and a subject point (i.e. instance to explain) that does not have the expected class are received. Multiple reference points that have the expected class are selected as starting points. Based on the subject point and the starting points, multiple discrete interpolated points are generated that have the expected class. Based on the subject point and the discrete interpolated points, multiple continuous interpolated points are generated that have the expected class. A counterfactual explanation of why the subject point does not have the expected class is directly generated based on continuous interpolated point(s) and, thus, indirectly generated based on the discrete interpolated points. For acceleration, neither way of interpolation (i.e. counterfactual generation) is iterative.

Abstract: Herein, a computer generates and evaluates many preprocessor configurations for a window preprocessor that transforms a training timeseries dataset for an ML model. With each preprocessor configuration, the window preprocessor is configured. The window preprocessor then converts the training timeseries dataset into a configuration-specific point-based dataset that is based on the preprocessor configuration. The ML model is trained based on the configuration-specific point-based dataset to calculate a score for the preprocessor configuration. Based on the scores of the many preprocessor configurations, an optimal preprocessor configuration is selected for finally configuring the window preprocessor, after which, the window preprocessor can optimally transform a new timeseries dataset such as in an offline or online production environment such as for real-time processing of a live streaming timeseries.

Abstract: The present invention relates to machine learning (ML) explainability (MLX). Herein are local explanation techniques for black box ML models based on coalitions of features in a dataset. In an embodiment, a computer receives a request to generate a local explanation of which coalitions of features caused an anomaly detector to detect an anomaly. During unsupervised generation of a new coalition, a first feature is randomly selected from features in a dataset. Which additional features in the dataset can join the coalition, because they have mutual information with the first feature that exceeds a threshold, is detected. For each feature that is not in the coalition, values of the feature are permuted in imperfect copies of original tuples in the dataset. An average anomaly score of the imperfect copies is measured. Based on the average anomaly score of the imperfect copies, a local explanation is generated that references (e.g. defines) the coalition.

Abstract: Herein is a universal anomaly threshold based on several labeled datasets and transformation of anomaly scores from one or more anomaly detectors. In an embodiment, a computer meta-learns from each anomaly detection algorithm and each labeled dataset as follows. A respective anomaly detector based on the anomaly detection algorithm is trained based on the dataset. The anomaly detector infers respective anomaly scores for tuples in the dataset. The following are ensured in the anomaly scores from the anomaly detector: i) regularity that an anomaly score of zero cannot indicate an anomaly and ii) normality that an inclusive range of zero to one contains the anomaly scores from the anomaly detector. A respective anomaly threshold is calculated for the anomaly scores from the anomaly detector. After all meta-learning, a universal anomaly threshold is calculated as an average of the anomaly thresholds. An anomaly is detected based on the universal anomaly threshold.

Abstract: In a computer, each of multiple anomaly detectors infers an anomaly score for each of many tuples. For each tuple, a synthetic label is generated that indicates for each anomaly detector: the anomaly detector, the anomaly score inferred by the anomaly detector for the tuple and, for each of multiple contamination factors, the contamination factor and, based on the contamination factor, a binary class of the anomaly score. For each particular anomaly detector excluding a best anomaly detector, a similarity score is measured for each contamination factor. The similarity score indicates how similar, between the particular anomaly detector and the best anomaly detector, are the binary classes of labels with that contamination factor. For each contamination factor, a combined similarity score is calculated based on the similarity scores for the contamination factor.

Abstract: A computer sorts empirical validation scores of validated training scenarios of an anomaly detector. Each training scenario has a dataset to train an instance of the anomaly detector that is configured with values for hyperparameters. Each dataset has values for metafeatures. For each predefined ranking percentage, a subset of best training scenarios is selected that consists of the ranking percentage of validated training scenarios having the highest empirical validation scores. Linear optimizers train to infer a value for a hyperparameter. Into many distinct unvalidated training scenarios, a scenario is generated that has metafeatures values and hyperparameters values that contains the value inferred for that hyperparameter by a linear optimizer. For each unvalidated training scenario, a validation score is inferred. A best linear optimizer is selected having a highest combined inferred validation score. For a new dataset, the best linear optimizer infers a value of that hyperparameter.

Abstract: Principal component analysis (PCA) accelerates and increases accuracy of genetic algorithms. In an embodiment, a computer generates many original chromosomes. Each original chromosome contains a sequence of original values. Each position in the sequences in the original chromosomes corresponds to only one respective distinct parameter in a set of parameters to be optimized. Based on the original chromosomes, many virtual chromosomes are generated. Each virtual chromosome contains a sequence of numeric values. Positions in the sequences in the virtual chromosomes do not correspond to only one respective distinct parameter in the set of parameters to be optimized. Based on the virtual chromosomes, many new chromosomes are generated. Each new chromosome contains a sequence of values. Each position in the sequences in the new chromosomes corresponds to only one respective distinct parameter in the set of parameters to be optimized. The computer may be configured based on a best new chromosome.

Abstract: The present invention relates to machine learning (ML) explainability (MLX). Herein are local explanation techniques for black box ML models based on coalitions of features in a dataset. In an embodiment, a computer receives a request to generate a local explanation of which coalitions of features caused an anomaly detector to detect an anomaly. During unsupervised generation of a new coalition, a first feature is randomly selected from features in a dataset. Which additional features in the dataset can join the coalition, because they have mutual information with the first feature that exceeds a threshold, is detected. For each feature that is not in the coalition, values of the feature are permuted in imperfect copies of original tuples in the dataset. An average anomaly score of the imperfect copies is measured. Based on the average anomaly score of the imperfect copies, a local explanation is generated that references (e.g. defines) the coalition.

Abstract: In an embodiment in a computer, each of several anomaly detectors infers a respective anomaly inference for each of many test tuples. For each available anomaly detector that is not the candidate anomaly detector, a respective fitness score is measured for the candidate anomaly detector that indicates how similar are anomaly inferences of the candidate anomaly detector to anomaly inferences of the available anomaly detector. Fitness scores of the candidate anomaly detector are combined into a combined fitness score for the candidate anomaly detector. The best anomaly detector that has a highest combined fitness score is selected for further operation such as inferring an anomaly inference for a new tuple while retraining or in production.

Abstract: Herein are timeseries preprocessing, model selection, and hyperparameter tuning techniques for forecasting development based on temporal statistics of a timeseries and a single feed-forward pass through a machine learning (ML) pipeline. In an embodiment, a computer hosts and operates the ML pipeline that automatically measures temporal statistic(s) of a timeseries. ML algorithm selection, cross validation, and hyperparameters tuning is based on the temporal statistics of the timeseries. The result from the ML pipeline is a rigorously trained and production ready ML model that is validated to have increased accuracy for multiple prediction horizons. Based on the temporal statistics, efficiency is achieved by asymmetry of investment of computer resources in the tuning and training of the most promising ML algorithm(s). Compared to other approaches, this ML pipeline produces a more accurate ML model for a given amount of computer resources and consumes fewer computer resources to achieve a given accuracy.

Abstract: Herein are acceleration and increased reliability based on classification and scoring techniques for machine learning that compare two similar datasets of different ages to detect data drift without a predefined drift threshold. Various subsets are randomly sampled from the datasets. The subsets are combined in various ways to generate subsets of various age mixtures. In an embodiment, ages are permuted and drift is detected based on whether or not fitness scores indicate that an age binary classifier is confused. In an embodiment, an anomaly detector measures outlier scores of two subsets of different age mixtures. Drift is detected when the outlier scores diverge. In a two-arm bandit embodiment, iterations randomly alternate between both datasets based on respective probabilities that are adjusted by a bandit reward based on outlier scores from an anomaly detector. Drift is detected based on the probability of the younger dataset.